openai_query

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must fully disclose behavioral traits. It reveals the underlying API (OpenAI Responses with web_search_preview) and default model, but omits important aspects like rate limits, token usage, cost implications, error handling, or whether the tool is read-only. This is insufficient for an agent to reliably invoke it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences, front-loading the core purpose and key technical detail. It earns its place without redundancy, though it could be slightly more informative without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (3 parameters, output schema exists), the description provides a basic overview but lacks depth on return format beyond 'answer + cited URLs' and parameter semantics. It is adequate for simple use cases but insufficient for nuanced invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It only mentions the default model, offering minimal insight into the 'model' parameter. The 'question' and 'system_prompt' parameters are not explained at all (no length limits, purpose, or how they affect behavior).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: ask ChatGPT with web search and return answer plus cited URLs. It specifies the verb 'ask', the resource 'ChatGPT (with web search)', and the output, effectively distinguishing it from sibling tools like claude_query or gemini_query which may not use web search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for questions requiring web search, providing clear context by mentioning 'with web search'. However, it lacks explicit guidance on when to use this tool versus alternatives like perplexity_query or multi_llm_query, and no exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.